I created a program interface that connects OPEN AI and LINE BOT, but how should I call and use my robot assistant or retrieve the files I uploaded? The current replies seem to be from the original OPEN AI chatbot. This problem has troubled me for a long time, can someone help me?
thank you very much
I use OPEN AI to connect to the LINE robot, and then want the robot to reply
with the data in the Vector Store in OPEN AI and then send it back. The connection has been normal.
But it can only reply to chat messages normally, but it cannot correctly reply to the data in the vector storage and then send it back.
How should I modify this?
In addition, how do I know the correct URL of a certain dataset in Vector Store??
I haven’t a clue. I do not even know what robot you speak of. I’m sure someone in here has the answer you seek.
I am a beginner in programming. I use the program but cannot Unable to retrieve Vector store data in OPENAI. Can you tell me where I am going wrong?
My code:
-- coding: utf-8 --
import openai
import os
import sys
import aiohttp
import requests
from datetime import datetime, timedelta
from fastapi import Request, FastAPI, HTTPException
from linebot import (
AsyncLineBotApi, WebhookParser
)
from linebot.aiohttp_async_http_client import AiohttpAsyncHttpClient
from linebot.exceptions import (
InvalidSignatureError
)
from linebot.models import (
MessageEvent, TextMessage, TextSendMessage,
)
from dotenv import load_dotenv, find_dotenv
import logging
設置日誌紀錄
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(name)
讀取環境變數
_ = load_dotenv(find_dotenv())
Dictionary to store user message counts and reset times
user_message_counts = {}
User daily limit
USER_DAILY_LIMIT = 5
def reset_user_count(user_id):
user_message_counts[user_id] = {
‘count’: 0,
‘reset_time’: datetime.now() + timedelta(days=1)
}
查詢 OpenAI Storage Vector Store
def search_vector_store(query):
vector_store_id = ‘vs_O4EC1xmZuHy3WiSlcmklQgsR’ # Vector Store ID
api_key = os.getenv(‘OPENAI_API_KEY’) # 確保使用環境變數中正確的 API key
if not api_key:
logger.error("API key is not set")
return None
url = f"https://api.openai.com/v1/vector_stores/{vector_store_id}"
payload = {
"query": query
}
headers = {
"Authorization": f"Bearer {api_key}",
"Content-Type": "application/json",
"OpenAI-Beta": "assistants=v2"
}
logger.info(f"Sending request to Vector Store with query: {query}")
response = requests.post(url, json=payload, headers=headers)
if response.status_code == 200:
logger.info(f"Response from Vector Store: {response.json()}")
return response.json() # 假設回應返回 JSON
else:
logger.error(f"Error: Failed to search Vector Store, HTTP code: {response.status_code}, error info: {response.text}")
return None
呼叫 OpenAI Chat API
async def call_openai_chat_api(user_message):
openai.api_key = os.getenv(‘OPENAI_API_KEY’) # 確保使用環境變數中正確的 API key
assistant_id = 'asst_HVKXE6R3ZcGb6oW6fDEpbdOi' # 指定助手 ID
# 首先檢查知識庫
vector_store_response = search_vector_store(user_message)
knowledge_content = ""
if vector_store_response and "results" in vector_store_response:
knowledge_items = vector_store_response["results"]
if knowledge_items:
# 整合知識庫資料
knowledge_content = "\n".join(item['content'] for item in knowledge_items)
# 組合最終訊息
user_message = f"{user_message}\n相關知識庫資料：\n{knowledge_content}" if knowledge_content else user_message
try:
response = await openai.ChatCompletion.acreate(
model="gpt-3.5-turbo",
messages=[
{"role": "system", "content": f"Assistant ID: {assistant_id}. 你是一個樂於助人的助手，請使用繁體中文回覆。"},
{"role": "user", "content": user_message}
]
)
logger.info(f"Response from OpenAI assistant: {response.choices[0]['message']['content']}")
return response.choices[0]['message']['content']
except Exception as e:
logger.error(f"Error calling OpenAI assistant: {e}")
return "Error: 系統出現錯誤，請稍後再試。"
獲取 channel_secret 和 channel_access_token
channel_secret = os.getenv(‘ChannelSecret’, None)
channel_access_token = os.getenv(‘ChannelAccessToken’, None)
if channel_secret is None:
logger.error(‘Specify LINE_CHANNEL_SECRET as environment variable.’)
sys.exit(1)
if channel_access_token is None:
logger.error(‘Specify LINE_CHANNEL_ACCESS_TOKEN as environment variable.’)
sys.exit(1)
Initialize LINE Bot Messaging API
app = FastAPI()
session = aiohttp.ClientSession()
async_http_client = AiohttpAsyncHttpClient(session)
line_bot_api = AsyncLineBotApi(channel_access_token, async_http_client)
parser = WebhookParser(channel_secret)
Introduction message
introduction_message = (
“我是彰化基督教醫院 內分泌科小助理，您有任何關於：糖尿病、高血壓及內分泌的相關問題都可以問我。”
)
@app.post(“/callback”)
async def handle_callback(request: Request):
signature = request.headers[‘X-Line-Signature’]
# get request body as text
body = await request.body()
logger.info(f"Request body: {body.decode()}")
body = body.decode()
try:
events = parser.parse(body, signature)
except InvalidSignatureError:
logger.error("Invalid signature")
raise HTTPException(status_code=400, detail="Invalid signature")
for event in events:
if not isinstance(event, MessageEvent):
continue
if not isinstance(event.message, TextMessage):
continue
user_id = event.source.user_id
user_message = event.message.text
logger.info(f"Received message from user {user_id}: {user_message}")
# 檢查訊息計數是否需要重置
if user_id in user_message_counts:
if datetime.now() >= user_message_counts[user_id]['reset_time']:
reset_user_count(user_id)
else:
reset_user_count(user_id)
# 檢查用戶是否超過每日限制
if user_message_counts[user_id]['count'] >= USER_DAILY_LIMIT:
logger.info(f"User {user_id} exceeded daily limit")
await line_bot_api.reply_message(
event.reply_token,
TextSendMessage(text="您今天的用量已經超過，請明天再詢問。")
)
continue
# 處理特殊請求（如介紹）
if "介紹" in user_message or "你是誰" in user_message:
await line_bot_api.reply_message(
event.reply_token,
TextSendMessage(text=introduction_message)
)
continue
# 呼叫 OpenAI 助手
result_text = await call_openai_chat_api(user_message)
# 更新用戶訊息計數
user_message_counts[user_id]['count'] += 1
# 回應用戶訊息
await line_bot_api.reply_message(
event.reply_token,
TextSendMessage(text=result_text)
)
return 'OK'